Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm

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[1] Noriega, A.
[2] Blanco, D.
[3] Alvarez, B.J.
[4] Garcia, A.
来源：
Blanco, D. (dbf@uniovi.es) | 1600年 / Springer London卷 / 69期
关键词：
Fused deposition modelling (FDM) is the most extended additive manufacturing technique up to date. In FDM; a thermoplastic material is extruded through a nozzle to form layers; and the final geometry is the result of consecutive superimposed layers. However; it is difficult to obtain an adequate dimensional accuracy for some applications due to the characteristics of the process. This paper proposes a method for increasing accuracy of the distance between parallel faces on FDM manufactured prismatic parts; consisting in replacing the theoretical values of CAD model dimensions by new optimized values. For this purpose; a model has been developed for predicting the dimensions of the manufactured parts; based on design characteristics. Particularly; this work has used an artificial neural network combined with an optimization algorithm; to determine the optimal dimensional values for the CAD model. Subsequently; CAD model is redesigned according to the dimensions provided by the optimization algorithm; and the part is manufactured. The results show that the application of this methodology allows for a reduction in manufacturing error of approximately 50 % for external dimensions and 30 % for internal dimensions. © Springer-Verlag London 2013;
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